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A Support Framework for Argumentative Discussions Management in the Web

  • Elena Cabrio
  • Serena Villata
  • Fabien Gandon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)

Abstract

On the Web, wiki-like platforms allow users to provide arguments in favor or against issues proposed by other users. The increasing content of these platforms as well as the high number of revisions of the content through pros and cons arguments make it difficult for community managers to understand and manage these discussions. In this paper, we propose an automatic framework to support the management of argumentative discussions in wiki-like platforms. Our framework is composed by (i) a natural language module, which automatically detects the arguments in natural language returning the relations among them, and (ii) an argumentation module, which provides the overall view of the argumentative discussion under the form of a directed graph highlighting the accepted arguments. Experiments on the history of Wikipedia show the feasibility of our approach.

Keywords

Community Manager Argumentation Framework Argumentative Discussion Natural Language Text Argumentation Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Cabrio, E., Magnini, B., Ivanova, A.: Extracting context-rich entailment rules from wikipedia revision history. In: The People’s Web Meets NLP Workshop (2012)Google Scholar
  2. 2.
    Cabrio, E., Villata, S.: Natural language arguments: A combined approach. In: European Conference on Artificial Intelligence (ECAI), pp. 205–210 (2012)Google Scholar
  3. 3.
    Carenini, G., Moore, J.D.: Generating and evaluating evaluative arguments. Artificial Intelligence 170(11), 925–952 (2006)CrossRefGoogle Scholar
  4. 4.
    Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics 22(2), 249–254 (1996)Google Scholar
  5. 5.
    Castro, A.G., Norena, A., Betancourt, A., Ragan, M.A.: Cognitive support for an argumentative structure during the ontology development process. In: Intl. Protege Conference (2006)Google Scholar
  6. 6.
    Cayrol, C., Lagasquie-Schiex, M.-C.: Bipolarity in argumentation graphs: Towards a better understanding. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 137–148. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Chesñevar, C.I., Maguitman, A.: An argumentative approach to assessing natural language usage based on the web corpus. In: European Conference on Artificial Intelligence (ECAI), pp. 581–585 (2004)Google Scholar
  8. 8.
    Dagan, I., Dolan, B., Magnini, B., Roth, D.: Recognizing textual entailment: Rational, evaluation and approaches. JNLE 15(04), i–xvii (2009)Google Scholar
  9. 9.
    Dung, P.: On the acceptability of arguments and its fundamental role in non-monotonic reasoning, logic programming and n-person games. Artificial Intelligence 77(2), 321–358 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Dutrey, C., Bouamor, H., Bernhard, D., Max, A.: Local modications and paraphrases in wikipedia’s revision history. SEPLN Journal 46, 51–58 (2011)Google Scholar
  11. 11.
    Heras, S., Atkinson, K., Botti, V.J., Grasso, F., Julián, V., McBurney, P.: How argumentation can enhance dialogues in social networks. In: Computational Model of Arguments (COMMA), pp. 267–274 (2010)Google Scholar
  12. 12.
    Kouylekov, M., Negri, M.: An open-source package for recognizing textual entailment. In: ACL System Demonstrations, pp. 42–47 (2010)Google Scholar
  13. 13.
    Lange, C., Bojars, U., Groza, T., Breslin, J., Handschuh, S.: Expressing argumentative discussions in social media sites. In: SDoW (2008)Google Scholar
  14. 14.
    Max, A., Wisniewski, G.: Mining naturally-occurring corrections and paraphrases from wikipedia’s revision history. In: LREC (2010)Google Scholar
  15. 15.
    Schneider, J., Groza, T., Passant, A.: A review of argumentation for the social semantic web. Semantic Web J. (2011)Google Scholar
  16. 16.
    Wyner, A., van Engers, T.: A framework for enriched, controlled on-line discussion forums for e-government policy-making. In: eGov (2010)Google Scholar
  17. 17.
    Yamangil, E., Nelken, R.: Mining wikipedia revision histories for improving sentence compression. In: Proc. of ACL (Short Papers), pp. 137–140 (2008)Google Scholar
  18. 18.
    Yatskar, M., Pang, B., Danescu-Niculescu-Mizil, C., Lee, L.: For the sake of simplicity: Unsupervised extraction of lexical simplifications from wikipedia. In: HLT-NAACL, pp. 365–368 (2010)Google Scholar
  19. 19.
    Zanzotto, F., Pennacchiotti, M.: Expanding textual entailment corpora from wikipedia using co-training. In: The People’s Web Meets NLP Workshop (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Elena Cabrio
    • 1
  • Serena Villata
    • 1
  • Fabien Gandon
    • 1
  1. 1.INRIA Sophia AntipolisFrance

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